mpw-006 / MM4 Metal 4

the MM4 shapes of all of mpw-006

40 of 40 dies rendered.

209,820,710 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 (351.6 MB in total, largest 11.5 MB), 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_MM4_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_MM4_reticle.png
the stitched reticle, full resolution
165.6 MB 331d6b1597c0cc8a918401fa4a0362cb16e2b58f8d3fda6654601b4b82deba5e04ecccb0 180df2881d70a7f675823693c5d686b991bbc3dc66547faab4815990d777c5c8
mpw-006_MM4_reticle_overview.png
the same image scaled to fit 4096 px
6.0 MB 63d23e3d843097813b4da3456377921ceb447e019acc4ab517ccf7bc063328e990a9d3dd 0ad8321cb56493dd14fce13ad9086968ce05b3967aab96962d52ed64ab915abf
mpw-006_MM4_tiles.json
where every tile belongs, and its checksum
13.6 kB 33133af45bdc8d8bc7d65610711aaa843a51ff090268d3fae6f4608b5bf790522a95d21f 484dcea89846e7bf384a6a4d1268a2e1837a2e91e9fced4c975dbd7506be646f
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 10.8 MB3737a13d0295b374631b9352f0507aa12c42d0e3c953221b546221aa31efaf15
A2 microwatt_mpw6 7576×107767576, 0 11.5 MB2936e16687d47083a8f023f3ca900b82b47846716254fdd805f2c211e2423f38
A3 hack_soc_-_mpw-6 7576×1077615152, 0 10.9 MBb4ed1a15d01079c143e292ec5a4a30babd70a78053e4fdc334ead9c951a6fba2
A4 riscduino-dcore_d3_ 7576×1077622728, 0 10.5 MB0f123a66bb481ba6ca490a7897b32bb829b2beeac791be2962124c6cbbcee894
A5 riscduino-qcore_q2_ 7576×1077630304, 0 11.1 MBd849b33acf7ae777935f71c7860b190094524444d98c77b53f7a749dcbe0c711
A6 riscduino-score-s4-2 7576×1077637880, 0 10.5 MB42c262d24c4a75b0f17367cb7e17c5325e184dcbcb9f545b9936a70757fc2237
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 sonaronchip8 7576×107760, 10776 8.4 MB93444543a7ee7d28fa0b1553fda4b414563242d4bb80a826b3027802229a50a4
B2 ft8_receiver_test 7576×107767576, 10776 3.9 MB0d9dca2394071315ca6ab61f0577a70da57e1de2edb1797528a189fc720a05b5
B3 azadi_iii_dft 7576×1077615152, 10776 10.7 MBc2726297818a77ebdfb59d428d52c1bc06cf4dffc54e7e5036bb3c8ee548807e
B4 sramtestvehiclesequel 7576×1077622728, 10776 3.6 MBdc1d1db9da544299d6502dbe5c4594c50ce1f256674e1eb8a2a3f60f37a44f8e
B5 isa_16_bit_microprocessor 7576×1077630304, 10776 10.3 MBf062c0528ae1495023cc9165c90600ea087483f2c529e5aab74660fb26c62b99
B6 asicle-ro 7576×1077637880, 10776 11.2 MBf496a355939a8438532660a2a0809fb9691a8ddbcabf6c525fc11e5cd9dd3d15
B7 10b_adc_and_analog_support 7976×1077645456, 10776 3.9 MB1c6284a50701e2be00fff4a4f88a7ac1d1760827684f444462879e3bd49d4723
C1 yonga-modbus_controller 7576×107760, 21552 10.8 MB044520a60ea95dfef21b6270edda18849196aaf7df66810299d2fee9ee3041df
C2 christmastreecontroller__mpw5_rerun_ 7576×107767576, 21552 10.4 MB3f10b5495355e3e64d8a6be6da8c8aeb4447374e34111654e5d42cbc14cd0b6c
C3 openram_test_design 7576×1077615152, 21552 10.6 MB5fd263703f9d11ffa36edaee849c197c816913d51a64a205ed53e15b0fed69b8
C4 pwm__resubmission_from_mpw-5_ 7576×1077622728, 21552 9.5 MB27440d6b5bbefb048fe5aba60a47c9a3dbd29a77f0a21f5ce5b49efa732ad300
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 hyperram_interface 7576×1077637880, 21552 10.4 MBb1afde470d4d20c4eff1f1fed7944c8451d59321899288ac0e911aa5b3b295a8
C7 adaptive_cordic-based_32-point_dct 7976×1077645456, 21552 11.0 MB2860d3a4c953e8b628c6b39ac98043bc9630ae4e7405dfab6a524a85097e6ed0
D1 cmos_cascode_dynamic_comparator 7576×107760, 32328 3.7 MBb5adf34e45fe24400a521b3c337b5fc70c7bc4c49daf37ab87579a9796ef48db
D2 cmos_high_speed_dynamic_comparator 7576×107767576, 32328 3.7 MBecddffdab8293720a7eb94aa3023a7500e96ce6b4b9684fecda7cd4fa0e6c4be
D3 order_prga_mpw6 7576×1077615152, 32328 9.8 MB950f74656b737a1f07cccf9c142da56939b03a01a412584517061a2c95333a69
D4 temporal_runtime_monitoring_component 7576×1077622728, 32328 10.8 MB39c4e29a91bb97dde5937ba54eda85f355f24bd618676b3b9537de8ffee69261
D5 large_memory_micro 7576×1077630304, 32328 11.0 MBfedbfb046f4e3fd2564284ecf862b3766d02257f8e7faa2ad9c62457505f24c9
D6 rng_chaos_scroll 7576×1077637880, 32328 10.3 MBa01c855689af44f580909bd2f59b085672a1e9b91e1b44f6cb5e1d1846a26a3d
D7 mixed_signal_circuits_mpw6 7976×1077645456, 32328 3.6 MB9063b125eec770fc71eb6e011463235b0d9572880b3c20bd4e6f48f837132607
E1 mixed_signal_circuits_v2 7576×107760, 43104 3.6 MBd94c564005a220863669fc6449fc6e6735811c0316f4a01d93ea995ad4834dbd
E2 direct_conversion_receiver_mpw6 7576×107767576, 43104 3.6 MB8728450406a160717c0dd28f20ac05c0be5d4cd71974b7a484e016f78a73cf14
E3 floating_point_halfprecision 7576×1077615152, 43104 10.8 MB64c482439ba9ac7f97e5dbb7238b37152637d9ace252a0e4ccdd4a027e302772
E4 floating_point_unit_bfloat16 7576×1077622728, 43104 10.9 MB8c936c10c3b996d121b2072149f3ceb4576b1926fe8d9a9e4cdfd7316010e58c
E5 floating_point_unit_single_precision 7576×1077630304, 43104 10.9 MB818a28a3c33aa76850d21d69b18fbf94ae3373cfc1db3ba75234af156ebd80ae
E6 experiar_soc 7576×1077637880, 43104 10.0 MBf683603577d83b8f36195cd5ebeb85a9514c7590d41729cae7df32904f4b6f44
E7 my_caravel_analog_project 7976×1077645456, 43104 3.8 MBe601cc2ed1683663e28b27df533bd01deb569d9086b716b5e658c288429eadc5
F1 efabless_mpw6_riscduino 7576×111760, 53880 9.7 MBafb4e6c108ed2237dd9aa622521f899525ca144268d31baf825f46f13eb2ccc1
F2 motius_pong 7576×111767576, 53880 10.7 MB9184a37ec8216c6f2d59d33d562a7631f745cf5525799fea7501ee7f3d7221c9
F3 soc_now 7576×1117615152, 53880 10.5 MB2894cf8aa9f191dc44da4d01ef8d58481f5cafae0ca4962c060f67baddcf3394
F4 ORDER_PRGA_MPW6_v2 7576×1117622728, 53880 9.8 MB88673280a36de83a353ce7c460dfe14c72328be38056ddd4bd657c364650f331
F5 optimised_strong_arm_latch 7576×1117630304, 53880 3.7 MBc1f52aed75a8966a366bb58f70aa28aecd29966375d34bf18b0b195d16a2b802
F6 riscduino_qcore_folk 7576×1117637880, 53880 9.5 MB8412e2e1146d672f3287e209c84c375db560e62fd2429e094f43de8a21aef289
F7 tensor_calculation_using_nngen 7976×1117645456, 53880 10.8 MBe7e1a0e876cc37d40331491cc1b2acdfd830e65a9a915a7446d199551b4d060e

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 MM4 on mpw-006

Resolved: reticle 5CS8014AC, run mpw-006, mask MM4

Mask layer
MM4 — Metal 4
Process step
154 — Metal mask4
Drawn layer
71:20
Layer on the mask-layer GDS
51:0
Fill layer
51:28
Also
Metal 4
Used in SKY130
yes

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.