mpw-007 / CAP2M Capacitor mask

the CAP2M shapes of all of mpw-007

40 of 40 dies rendered.

120,440 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 (4.5 MB in total, largest 208.4 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-007_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-007_CAP2M_reticle.png
the stitched reticle, full resolution
3.6 MB a39a64455def2ea45ea0074b8615f50135123d23d1a2a32c02b78a0838983de596d1f4a5 ee2f41f5ef7a0554904a562e237a3f53b725ae5163e456c0e00c284285bf09a3
mpw-007_CAP2M_reticle_overview.png
the same image scaled to fit 4096 px
53.1 kB c90a5f8303767a0268b8ba857d774248970f1c40d7d19fb2e3370c6b2a168916872a1839 bb2f42f806178bbbbd90830fdabf6677c3055c954061d63dab6cfccd6abaacf4
mpw-007_CAP2M_tiles.json
where every tile belongs, and its checksum
13.6 kB 85861ac52045d30a4c4fa440c7c491270e068306689997badbf60f17a44db66d0c7fb3d0 834ca43993ee7f0e55c1a4eacff47920af976c0687f28e8d35ab309a616814e5
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 tinytapeout 7576×107760, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A3 microwatt_mpw7 7576×1077615152, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A4 yonga-can_controller 7576×1077622728, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A5 rocketalpha 7576×1077630304, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A6 riscduino-dcore_d3_3 7576×1077637880, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B2 riscduino-score-s4-3 7576×107767576, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B4 sramtestvehicleg3 7576×1077622728, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 103.7 kB46fc177b816440543c84c1308d00c55df962cec8beb3d82c4e8943571a91cff9
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B7 reram_crossbar 7976×1077645456, 10776 208.4 kB376a860bcb93be64045dc69f13580edbd5a9e4e2a2126dda3b06b80db87345b4
C1 trainable-nn 7576×107760, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 108.4 kB7eec85d7921c3e369f7b2109ae67390b0da1f39c13521ebbc076c9da9e753ca4
C3 bitcoin_mining_asics 7576×1077615152, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C4 yonga-mcu 7576×1077622728, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
D1 mixed_signal_circuits-jun13 7576×107760, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D2 fpga_programming_management_unit 7576×107767576, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D3 warp-v 7576×1077615152, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D4 leaf__mpw7_ 7576×1077622728, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D5 in_memory_computing_sram 7576×1077630304, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E2 in_memory_computing_rram 7576×107767576, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E3 rram_imc_v2_flat 7576×1077615152, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E4 topmetalse-dps 7576×1077622728, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E5 chaos_automaton 7576×1077630304, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E6 hehecore_resubmission 7576×1077637880, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E7 waveform_generator 7976×1077645456, 43104 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
F1 rift2core 7576×111760, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F2 rift2go_2300 7576×111767576, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F3 sloci_resubmission 7576×1117615152, 53880 107.8 kBa303eb1e7be77e037c4496d2df256b03826291bc61e152af55089c8f20b3403d
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F5 crypto_aes128 7576×1117630304, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F6 graphics_controller 7576×1117637880, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F7 prga-test 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-007

Resolved: reticle 5CS8016AC, run mpw-007, mask CAP2M

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

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.