mpw-005 / VIM4 Via4

the VIM4 shapes of all of mpw-005

40 of 40 dies rendered.

9,759,787 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 (28.7 MB in total, largest 850.9 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-005_VIM4_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-005_VIM4_reticle.png
the stitched reticle, full resolution
11.5 MB 9ef16806dd76a0b4b771321c041b2918a6cc2ee9a2eaff8cd73e700c1cf98b4787fff739 0917395a5820058b6974bdaac541ae2146c2ac4aadd6379bc3263b2e1a7dff98
mpw-005_VIM4_reticle_overview.png
the same image scaled to fit 4096 px
579.5 kB c2662a72273ed14b2ff476839816bb16e420a566e75a5d77981017d1313e5233c250e4a0 c2e70e4039c945211a20b4f7d8e69302538c57f135714cbd93e30f4649c2e32e
mpw-005_VIM4_tiles.json
where every tile belongs, and its checksum
13.5 kB fa915f7a23cebe295360d9f8ddfe89c354cde4a16092caa1dc7d512a451accefd433158d 1e5e1232fdb88642160db3443dae54b034c1800e7374b4e0b2d7449099b64064
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 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 662.9 kB0a80beccc3295ae70a2b6bb8cc50bd4fd439038835b17c19b31d542b90d47c3b
A2 mpw5_cache 7576×107767576, 0 721.9 kBb968c5fa66f2e47fea58d6f62760108fb20a31168edb36fe01bf93170a9cc7a2
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 721.3 kB368b638b3d93fa8b65307a74dcb8ad2f5921423de2cf8cf224b4771639655726
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 721.9 kB86ad6d3346bd07cae3fed738d28aa815dfbd116ac394647569bb12b8a7fede0c
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 722.4 kBb262c353e17f1d6b662ae1edc2bfa2651fbec4f6d50414d3236f3f0e32540987
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 721.9 kB764c78ed3fe65f4af9f442679d18b2773bdd650e6c018ce9fb3788cb4e4d58d1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 721.6 kB90b8bf904df46b5e35dd91948b2b3a9291b0f896ae8737e0612484ffafb71f3e
B2 microwatt_mpw5 7576×107767576, 10776 721.6 kB6cd93b7ba0a2bda38bf5cd769cd8c454fa293146c2c9fff3addd2a25d9bb00c8
B3 armleo_gpio_mpw5 7576×1077615152, 10776 671.0 kB8dfed7a745b45d0f0045f7c0f1de3b9a671a3ff6461dec69147e54b648a4bced
B4 fabulous_efpga 7576×1077622728, 10776 712.6 kBb8bcc5dbfc5d746bb9134f0b55789dab1563a6b7852f404b9732970ed253609b
B5 i2c_controller 7576×1077630304, 10776 721.9 kBa15be73535373672b6db1d0438f050c54566b8ca50424d4c8a9b6b07d71bf925
B6 lbist-mbist 7576×1077637880, 10776 698.1 kB48e773251d7882aa15d8aafb8662c59e3abb6c0f814fb653871c21e28436ae56
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 722.3 kBe3cdbc7d49fcb47566ba5d28ac199628e69e422158f77d8b4862d87b328544d9
C1 riscduino-qcore__q0_ 7576×107760, 21552 707.1 kBa76d15fb4c6164f866970d9ea28b9cd8fc1e008b6410ab8556dee39dfd1ad883
C2 high_speed_vco 7576×107767576, 21552 661.3 kBcd3597700cca4f3ee2e837544c2c756bb82689bf2572a82cbcc226fefc3aa05f
C3 openfasoc-cryo-gen 7576×1077615152, 21552 850.9 kB587d1a40445ae802a23894503f14cd6ed3c206a334414bf27f96d5f6aaa14d6f
C4 microwave_signal_generator 7576×1077622728, 21552 663.2 kB3cb870362bbd5d23295df87bd6ff7b647a50190f1cea6bbcbf6c4dc5c96125a0
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 719.6 kB9254f846b0f387b9a4db134835507a4fa8d6d366292c73952893d6cd502e7360
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 687.0 kBb13b7ffeecfe5f0bf72bd9af591310e028327e50a1bb52d23ffc770955a4a192
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 673.3 kB10e42b16e1fc6627b0e8d8cf82d8469871296a5c3c454de1c4fd30adfa3fce1b
D2 azadi_iii 7576×107767576, 32328 715.8 kBfddcb69af9b69d5d864f5a2b015bac41de6a8aa520630400bb3a335b7b64910a
D3 tia_for_physic_experiments 7576×1077615152, 32328 691.2 kBf4531bea2bf546571b4abaabb4603df07f2c7954169d9b253950f2bc3d571d0e
D4 qf105 7576×1077622728, 32328 719.9 kBbc79b5f40ded228a557e6f5692eca1a2ecd06d17b531f521a39dbe03ef1aa564
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 703.0 kB071fb285a4ec5a2b00339b0f292ae29dfb53326cfe8d6acd5f28a36c1abce8fa
D6 junga_soc_mpw5 7576×1077637880, 32328 694.8 kBe6259e9ef560df601f1291c15c4c1d1c2ca7594043f42d777b7144dcc21c86a6
D7 nand_flash_mpw-5 7976×1077645456, 32328 736.6 kBe8dce77fc969c7a12c09b4e6c2dfd0111f5f7f2a8c7495c54da0b5d9066bdc7d
E1 kasirga-c0-mpw-5 7576×107760, 43104 720.6 kB120dd6404284ba43c3a4086850e6e2bade237dcdbc85b6c4701862bd08f29dbf
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 720.2 kB0c5c12539c8f19523a24ddd5fe6c69a8a597be732e60fb32755544c3d247c984
E3 wirelessenergyharvesting 7576×1077615152, 43104 661.5 kBccbe97067b1d6d59193ebb14e786b5e5384a498c28022298fe51b6b0d918daf5
E4 caravel_raster_engine 7576×1077622728, 43104 720.4 kBae52795f054f9d46a2f9731ae716ff081aeca3925d74f2f36c63b80a3940efa0
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 661.3 kBfc433d36275da645fbecc7050ec9f103d546f7e7421343c47f4457b3d59b8a3c
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 721.3 kB58ac0481c33cd37d9eada26da5fc67a0b910e73689ee4ac0044d810397466e30
E7 alu 7976×1077645456, 43104 736.4 kBddf8257e845e3db50612d071c494ca8c2ae70f4ff49591b366b5c681a246b04b
F1 4ft4 7576×111760, 53880 726.3 kB4d2c20aef9d0819f7d3bf678170be3e3785aa01b413e6043f2628de57d36ad2f
F2 rng_chaos 7576×111767576, 53880 726.5 kB79db82802d44c9300065cfac655f33a6a290ca625fc2c6aaaddf2f996fa86f16
F3 uetrv-ecore 7576×1117615152, 53880 724.5 kBa699160101108dd1ee8e826610569e18b1040adcd6b06ae5b7dce13a1118caf0
F4 systolic_array 7576×1117622728, 53880 726.3 kBc2d9a2a4f0a9c5fc2ccdcd10faa6b5323d70d853a8d77d9399d9218346056c24
F5 reram_test 7576×1117630304, 53880 666.2 kB1993b6a3bd54dfd5f0e1c1b2c0b664c21cf9a7e8d20d414f50ba7eab14fd031a
F6 secure_memory 7576×1117637880, 53880 726.0 kB66620ac2a2dccf40842eae8cb95b57253bc29dcbf1bf1c072527dbfc98b0e658
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 741.6 kBb5aa8547c9c63a9fcc2926172d3e2d18946a069bf3e9e81c438f92b6aa9319ac

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 VIM4 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask VIM4

Mask layer
VIM4 — Via4
Process step
159 — Via pad mask
Drawn layer
71:44
Layer on the mask-layer GDS
58:0
Also
Via 4 (M4→M5)
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/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.