mpw-006 / CAP2M Capacitor mask

the CAP2M shapes of all of mpw-006

40 of 40 dies rendered.

1,154 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.4 MB in total, largest 112.5 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-006_CAP2M_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-006_CAP2M_reticle.png
the stitched reticle, full resolution
3.5 MB 40de671d75f812bbb8c5fb17016334b95efcb5a5ac157e03683c215eb1adfe0127ac7355 d89ea554cdf703d238f7d0d42d2f093780ca88152f5924ff4e0d2092e65ec5e2
mpw-006_CAP2M_reticle_overview.png
the same image scaled to fit 4096 px
20.3 kB 6a70da93d501b87ee6a55d5800855d128c067b899bb423d24240c33594f2463d5861114f 57093a1da97990cc57295fda6a48e2220feb182b221bac96b09d974c61bd02f9
mpw-006_CAP2M_tiles.json
where every tile belongs, and its checksum
13.7 kB 013341c259f92b5b1085d61fa9751d688f3125a1256e590c4b0d32b430e477ccc05263a5 070192590df7fa60f2fc6fbfe9699f12a298515d93dd64dbc44ce1acfbe0dff4
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 free_and_open_source_silicon_implementation_of_aes 7576×107760, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A2 microwatt_mpw6 7576×107767576, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A3 hack_soc_-_mpw-6 7576×1077615152, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A4 riscduino-dcore_d3_ 7576×1077622728, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A5 riscduino-qcore_q2_ 7576×1077630304, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A6 riscduino-score-s4-2 7576×1077637880, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 sonaronchip8 7576×107760, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B2 ft8_receiver_test 7576×107767576, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B3 azadi_iii_dft 7576×1077615152, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B4 sramtestvehiclesequel 7576×1077622728, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B5 isa_16_bit_microprocessor 7576×1077630304, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B6 asicle-ro 7576×1077637880, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B7 10b_adc_and_analog_support 7976×1077645456, 10776 112.5 kB547fd9b55a075740c0bc33b96b9166fcb86a476a23551da68a5d615ce48d872c
C1 yonga-modbus_controller 7576×107760, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C2 christmastreecontroller__mpw5_rerun_ 7576×107767576, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C3 openram_test_design 7576×1077615152, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C4 pwm__resubmission_from_mpw-5_ 7576×1077622728, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 hyperram_interface 7576×1077637880, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C7 adaptive_cordic-based_32-point_dct 7976×1077645456, 21552 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
D1 cmos_cascode_dynamic_comparator 7576×107760, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D2 cmos_high_speed_dynamic_comparator 7576×107767576, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D3 order_prga_mpw6 7576×1077615152, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D4 temporal_runtime_monitoring_component 7576×1077622728, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D5 large_memory_micro 7576×1077630304, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D6 rng_chaos_scroll 7576×1077637880, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D7 mixed_signal_circuits_mpw6 7976×1077645456, 32328 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
E1 mixed_signal_circuits_v2 7576×107760, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E2 direct_conversion_receiver_mpw6 7576×107767576, 43104 103.1 kB5ca4288702b2f3f12c1447aaf85554e48fc119e12a9c6ddef410e97f804163d7
E3 floating_point_halfprecision 7576×1077615152, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E4 floating_point_unit_bfloat16 7576×1077622728, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E5 floating_point_unit_single_precision 7576×1077630304, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E6 experiar_soc 7576×1077637880, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E7 my_caravel_analog_project 7976×1077645456, 43104 112.1 kBdf29873ab0e6d7403c625734a28836e885258fc69c3b07d94c3b36968f0a8d3e
F1 efabless_mpw6_riscduino 7576×111760, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F2 motius_pong 7576×111767576, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F3 soc_now 7576×1117615152, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F4 ORDER_PRGA_MPW6_v2 7576×1117622728, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F5 optimised_strong_arm_latch 7576×1117630304, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F6 riscduino_qcore_folk 7576×1117637880, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F7 tensor_calculation_using_nngen 7976×1117645456, 53880 110.9 kBea0be884384754165c29bf0468e1ee1a1f67ad1c71ab09fb6c962f232b604ebe

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About CAP2M on mpw-006

Resolved: reticle 5CS8014AC, run mpw-006, mask CAP2M

Mask layer
CAP2M — Capacitor mask
Process step
152 — Capacitor mask
Drawn layer
97:44

mpw-006 — SKY130 MPW-6

Foundry product
EF MPW#6 (+ 'With ReRAM' split lot)
Reticle set
5CS8014AC
Run date
2022-04-11
Submissions closed
2022-06-08
Projects
85
Fab lot
Identification
confirmed — Lot records: EF MPW#6 ('Held pre-mask' in late 2022; MPW-6 taped out 2022-12)
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-6 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-006/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.